Lei A. Clifton

Efficacy of cognitive behavioural therapy for sleep improvement in patients with persistent delusions and hallucinations (BEST): a prospective, assessor-blind, randomised controlled pilot trial.

Summary Background Sleep disturbance occurs in most patients with delusions or hallucinations and should be treated as a clinical problem in its own right. However, cognitive behavioural therapy (CBT)—the best evidence-based treatment for insomnia—has not been tested in this patient population. We aimed to pilot procedures for a randomised trial testing CBT for sleep problems in patients with current psychotic experiences, and to provide a preliminary assessment of potential benefit. Methods We did this prospective, assessor-blind, randomised controlled pilot trial (Better Sleep Trial [BEST]) at two mental health centres in the UK. Patients (aged 18–65 years) with persistent distressing delusions or hallucinations in the context of insomnia and a schizophrenia spectrum diagnosis were randomly assigned (1:1), via a web-based randomisation system with minimisation to balance for sex, insomnia severity, and psychotic experiences, to receive either eight sessions of CBT plus standard care (medication and contact with the local clinical team) or standard care alone. Research assessors were masked to group allocation. Assessment of outcome was done at weeks 0, 12 (post-treatment), and 24 (follow-up). The primary efficacy outcomes were insomnia assessed by the Insomnia Severity Index (ISI) and delusions and hallucinations assessed by the Psychotic Symptoms Rating Scale (PSYRATS) at week 12. We did analysis by intention to treat, with an aim to provide confidence interval estimation of treatment effects. This study is registered with ISRCTN, number 33695128. Findings Between Dec 14, 2012, and May 22, 2013, and Nov 7, 2013, and Aug 26, 2014, we randomly assigned 50 patients to receive CBT plus standard care (n=24) or standard care alone (n=26). The last assessments were completed on Feb 10, 2015. 48 (96%) patients provided follow-up data. 23 (96%) patients offered CBT took up the intervention. Compared with standard care, CBT led to reductions in insomnia in the large effect size range at week 12 (adjusted mean difference 6·1, 95% CI 3·0–9·2, effect size d=1·9). By week 12, nine (41%) of 22 patients receiving CBT and one (4%) of 25 patients receiving standard care alone no longer had insomnia, with ISI scores lower than the cutoff for insomnia. The treatment effect estimation for CBT covered a range from reducing but also increasing delusions (adjusted mean difference 0·3, 95% CI −2·0 to 2·6) and hallucinations (−1·9, −6·5 to 2·7). Three patients, all in the CBT group, had five adverse events, although none were regarded as related to study treatment. Interpretation Our findings show that CBT for insomnia might be highly effective for improving sleep in patients with persistent delusions or hallucinations. A larger, suitably powered phase 3 study is now needed to provide a precise estimate of the effects of CBT for sleep problems, both on sleep and psychotic experiences. Funding Research for Patient Benefit Programme, National Institute for Health Research.

Gaussian Processes for Personalized e-Health Monitoring With Wearable Sensors

Advances in wearable sensing and communications infrastructure have allowed the widespread development of prototype medical devices for patient monitoring. However, such devices have not penetrated into clinical practice, primarily due to a lack of research into “intelligent” analysis methods that are sufficiently robust to support large-scale deployment. Existing systems are typically plagued by large false-alarm rates, and an inability to cope with sensor artifact in a principled manner. This paper has two aims: 1) proposal of a novel, patient-personalized system for analysis and inference in the presence of data uncertainty, typically caused by sensor artifact and data incompleteness; 2) demonstration of the method using a large-scale clinical study in which 200 patients have been monitored using the proposed system. This latter provides much-needed evidence that personalized e-health monitoring is feasible within an actual clinical environment, at scale, and that the method is capable of improving patient outcomes via personalized healthcare.

Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors

The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing physiological data to be acquired from mobile patients, little work has been undertaken on the use of the resultant data in a principled manner for robust patient care, including predictive monitoring. Most current devices generate so many false-positive alerts that devices cannot be used for routine clinical practice. This paper explores principled machine learning approaches to interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided. We adopt a one-class support vector machine formulation, proposing a formulation for determining the free parameters of the model using partial area under the ROC curve, a method arising from the unique requirements of performing online analysis with data from patient-worn sensors. There are few clinical evaluations of machine learning techniques in the literature, so we present results from a study at the Oxford University Hospitals NHS Trust devised to investigate the large-scale clinical use of patient-worn sensors for predictive monitoring in a ward with a high incidence of patient mortality. We show that our system can combine routine manual observations made by clinical staff with the continuous data acquired from wearable sensors. Practical considerations and recommendations based on our experiences of this clinical study are discussed, in the context of a framework for personalized monitoring.

Multitask Gaussian Processes for Multivariate Physiological Time-Series Analysis

Gaussian process (GP) models are a flexible means of performing nonparametric Bayesian regression. However, GP models in healthcare are often only used to model a single univariate output time series, denoted as single-task GPs (STGP). Due to an increasing prevalence of sensors in healthcare settings, there is an urgent need for robust multivariate time-series tools. Here, we propose a method using multitask GPs (MTGPs) which can model multiple correlated multivariate physiological time series simultaneously. The flexible MTGP framework can learn the correlation between multiple signals even though they might be sampled at different frequencies and have training sets available for different intervals. Furthermore, prior knowledge of any relationship between the time series such as delays and temporal behavior can be easily integrated. A novel normalization is proposed to allow interpretation of the various hyperparameters used in the MTGP. We investigate MTGPs for physiological monitoring with synthetic data sets and two real-world problems from the field of patient monitoring and radiotherapy. The results are compared with standard Gaussian processes and other existing methods in the respective biomedical application areas. In both cases, we show that our framework learned the correlation between physiological time series efficiently, outperforming the existing state of the art.

Probabilistic Novelty Detection With Support Vector Machines

Novelty detection, or one-class classification, is of particular use in the analysis of high-integrity systems, in which examples of failure are rare in comparison with the number of examples of stable behaviour, such that a conventional multi-class classification approach cannot be taken. Support Vector Machines (SVMs) are a popular means of performing novelty detection, and it is conventional practice to use a train-validate-test approach, often involving cross-validation, to train the one-class SVM, and then select appropriate values for its parameters. An alternative method, used with multi-class SVMs, is to calibrate the SVM output into conditional class probabilities. A probabilistic approach offers many advantages over the conventional method, including the facility to select automatically a probabilistic novelty threshold. The contributions of this paper are (i) the development of a probabilistic calibration technique for one-class SVMs, such that on-line novelty detection may be performed in a probabilistic manner; and (ii) the demonstration of the advantages of the proposed method (in comparison to the conventional one-class SVM methodology) using case studies, in which one-class probabilistic SVMs are used to perform condition monitoring of a high-integrity industrial combustion plant, and in detecting deterioration in patient physiological condition during patient vital-sign monitoring.

An Extreme Function Theory for Novelty Detection

We introduce an extreme function theory as a novel method by which probabilistic novelty detection may be performed with functions, where the functions are represented by time-series of (potentially multivariate) discrete observations. We set the method within the framework of Gaussian processes (GP), which offers a convenient means of constructing a distribution over functions. Whereas conventional novelty detection methods aim to identify individually extreme data points, with respect to a model of normality constructed using examples of “normal” data points, the proposed method aims to identify extreme functions, with respect to a model of normality constructed using examples of “normal” functions, where those functions are represented by time-series of observations. The method is illustrated using synthetic data, physiological data acquired from a large clinical trial, and a benchmark time-series dataset.

Modelling physiological deterioration in post-operative patient vital-sign data

Patients who undergo upper-gastrointestinal surgery have a high incidence of post-operative complications, often requiring admission to the intensive care unit several days after surgery. A dataset comprising observational vital-sign data from 171 post-operative patients taking part in a two-phase clinical trial at the Oxford Cancer Centre, was used to explore the trajectory of patients’ vital-sign changes during their stay in the post-operative ward using both univariate and multivariate analyses. A model of normality based vital-sign data from patients who had a “normal” recovery was constructed using a kernel density estimate, and tested with “abnormal” data from patients who deteriorated sufficiently to be re-admitted to the intensive care unit. The vital-sign distributions from “normal” patients were found to vary over time from admission to the post-operative ward to their discharge home, but no significant changes in their distributions were observed from halfway through their stay on the ward to the time of discharge. The model of normality identified patient deterioration when tested with unseen “abnormal” data, suggesting that such techniques may be used to provide early warning of adverse physiological events.

Support vector machine in novelty detection for multi-channel combustion data

Multi-channel combustion data, consisting of gas pressure and two combustion chamber luminosity measurements, are investigated in the prediction of combustion instability. Wavelet analysis is used for feature extraction. A SVM approach is applied for novelty detection and the construction of a model of normal system operation. Novelty scores generated by classifiers from different channels are combined to give a final decision of data novelty. Comparisons between the proposed SVM method and a GMM approach show that earlier identification of combustion instability, and greater distinction between stable and unstable data classes, are achieved with the proposed SVM approach.

'Errors' and omissions in paper-based early warning scores: the association with changes in vital signs—a database analysis

Objectives To understand factors associated with errors using an established paper-based early warning score (EWS) system. We investigated the types of error, where they are most likely to occur, and whether ‘errors’ can predict subsequent changes in patient vital signs. Methods Retrospective analysis of prospectively collected early warning system database from a single large UK teaching hospital. Results 16 795 observation sets, from 200 postsurgical patients, were collected. Incomplete observation sets were more likely to contain observations which should have led to an alert than complete observation sets (15.1% vs 7.6%, p<0.001), but less likely to have an alerting score correctly calculated (38.8% vs 30.0%, p<0.001). Mis-scoring was much more common when leaving a sequence of three or more consecutive observation sets with aggregate scores of 0 (55.3%) than within the sequence (3.0%, p<0.001). Observation sets that ‘incorrectly’ alerted were more frequently followed by a correctly alerting observation set than error-free non-alerting observation sets (14.7% vs 4.2%, p<0.001). Observation sets that ‘incorrectly’ did not alert were more frequently followed by an observation set that did not alert than error-free alerting observation sets (73.2% vs 45.8%, p<0.001). Conclusions Missed alerts are particularly common in incomplete observation sets and when a patient first becomes unstable. Observation sets that ‘incorrectly’ alert or ‘incorrectly’ do not alert are highly predictive of the next observation set, suggesting that clinical staff detect both deterioration and improvement in advance of the EWS system by using information not currently encoded within it. Work is urgently needed to understand how best to capture this information.

A Large-Scale Clinical Validation of an Integrated Monitoring System in the Emergency Department

We consider an integrated patient monitoring system, combining electronic patient records with high-rate acquisition of patient physiological data. There remain many challenges in increasing the robustness of “e-health” applications to a level at which they are clinically useful, particularly in the use of automated algorithms used to detect and cope with artifact in data contained within the electronic patient record, and in analyzing and communicating the resultant data for reporting to clinicians. There is a consequential “plague of pilots,” in which engineering prototype systems do not enter into clinical use. This paper describes an approach in which, for the first time, the Emergency Department (ED) of a major research hospital has adopted such systems for use during a large clinical trial. We describe the disadvantages of existing evaluation metrics when applied to such large trials, and propose a solution suitable for large-scale validation. We demonstrate that machine learning technologies embedded within healthcare information systems can provide clinical benefit, with the potential to improve patient outcomes in the busy environment of a major ED and other high-dependence areas of patient care.